Neuro-fuzzy-integrated data processing system

Abstract

The present invention relates to a data processing system in a hierarchical network configuration for executing applicable data processes in a comprehensible and executable form. The present invention allows data processing capabilities to be established with high precision in a short time based on a fuzzy-neuro-integrated concept. A fuzzy model is generated by a data processing system in the form of membership functions and fuzzy rules as technical information relating to a control target. According to this fuzzy model, a weight value of the connection between neurons is set and a pre-wired neural network is established. Then, the data of the control target are learned by the neural network. The connection state and a weight value of the neural network after the learning enable tuning of the fuzzy model.

What is claimed is: 
     
       1. A neuro-fuzzy integrated data processing system, comprising: an input part for receiving at least one input;   an antecedent membership function realizing part receiving an input from the input part and outputting at least one grade value indicating the applicability of a previous membership function corresponding to at least one of said input signals;   a rule part operatively coupled to said antecedent membership function realizing part for forming a network having at least one layer, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of at least one consequent membership function corresponding to at least one system output signal as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part operatively coupled to said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal.   
     
     
       2. A neuro-fuzzy integrated data processing system according to claim 1, wherein said rule part has a plurality of layers and is pre-wired between adjacent layers in said rule part, between said antecedent membership function realizing part and said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part, wherein the existence of a connection and one of a part and the whole of a weight value are set according to a fuzzy rule.   
     
     
       3. A neuro-fuzzy integrated data processing system according to claim 1, wherein said rule part has a plurality of layers and is completely connected between adjacent layers in said rule part, between said antecedent membership function realizing part and said rule part, and between said rule part and said consequence membership function realizing/non-fuzzy-processing part, wherein the existence of connection and a part or the whole of a weight value are set according to a fuzzy rule.   
     
     
       4. A neuro-fuzzy integrated data processing system according to claim 1 wherein said antecedent membership function realizing part comprises a hierarchical layered network.   
     
     
       5. A neuro-fuzzy integrated data processing system according to claim 4, wherein said antecedent membership function realizing part includes a unit for outputting a grade value of the applicability of said antecedent membership function, and outputs a grade value of a membership function indicating "an input signal is SMALL", with a sign of a weight value of the input connection to said unit and a sign of a threshold assumed as negative.   
     
     
       6. A neuro-fuzzy integrated data processing system according to claim 4, wherein said antecedent membership function realizing part includes a unit for outputting a grade value of the applicability of said antecedent membership function, and outputs a grade value of a membership function indicating "an input signal is LARGE", with a sign of a weight value of input connection to said unit and a sign of a threshold assumed as positive.   
     
     
       7. A neuro-fuzzy integrated data processing system according to claim 4, wherein said antecedent membership function realizing part includes a unit to output a grade value of a membership function indicating "an input signal is NORMAL", said unit comprising:   two units for receiving said input signal and outputting a Sigmoid function of said input signal corresponding to a weight value and a threshold of each input signal; and   a unit for obtaining the difference in the output of said two units and for outputting said grade value of said antecedent membership function.   
     
     
       8. A neuro-fuzzy integrated data system according to claim 1, wherein said rule part includes a part of a unit to calculate one of a logical operation, in response to two input values, a logical product, a logical sum, an acuity product, an acuity sum, an average, a permanent TRUE, and a permanent FALSE, and to calculate in a logical operation in response to one of two input values.   
     
     
       9. A neuro-fuzzy integrated data processing system according to claim 1, wherein said rule part includes a part of a unit for setting S as positive and t as between 0 and 1 in response to a plurality of input values X 1 , X 2 , . . . , X n  and for determining each of weight values W 1 , W 2 , . . . , W n  of connection and a threshold θ in response to a plurality of said input values as follows: ##EQU75## and thereby performing addition of a plurality of said input values as follows: ##EQU76##   
     
     
       10. A neuro-fuzzy integrated data processing system according to claim 1, wherein said rule part includes a part of a unit for setting S as positive and t as between 0 and 1 in response to a plurality of input values X 1 , X 2 , . . . , X n  and for determining each of weight values W 1 , W 2 , . . . , W n  of connection and a threshold θ in response to a plurality of said input values as follows: ##EQU77## and thereby performing multiplication of a plurality of said input values as follows: ##EQU78##   
     
     
       11. A neuro-fuzzy integrated data processing system according to claim 1, wherein said consequent membership function realizing/non-fuzzy-processing part comprises:   at least one consequent membership function realizing part for enlarging/reducing said consequent membership function corresponding to each of at least one of system output signals according to said enlargement/reduction rate of a consequent membership function provided by said rule part; and   at least one non-fuzzy-process calculation realizing part for calculating, using output values provided by said at least one consequent membership function realizing part, a representative value as said corresponding system output signal.   
     
     
       12. A neuro-fuzzy integrated data processing system according to claim 11, wherein said at least one consequent membership function realizing part comprises a hierarchical neural network having at least one layer where connection between adjacent layers in said hierarchical neural network, and a weight value of said connection is set corresponding to a consequent membership function.   
     
     
       13. A neuro-fuzzy integrated data processing system according to claim 12, wherein said at least one consequent membership function realizing part comprises a plurality of units each corresponding to a plurality of abscissa values of a curve of said consequent membership function; and   a grade value of said consequent membership function at each of said abscissa values is set, in said rule part, as a weight value of each connection between each unit for outputting an enlargement/reduction rate of at least one consequent membership function related to said corresponding system output signals and a plurality of said units.   
     
     
       14. A neuro-fuzzy integrated data processing system according to claim 12, wherein said at least one consequent membership function realizing part comprises a plurality of units each corresponding to a plurality of abscissa values of a curve of said consequent membership function; and   in said rule part, a value of 1 is set for the connection to a unit corresponding to an abscissa value of a consequent membership function curve specified by the consequent part of each of said fuzzy rules, and a value of 0 is set for other connection as a weight value of each connection between each unit for outputting a grade value of a fuzzy rule of at least one consequent membership function related to said corresponding system output signals and a plurality of said units.   
     
     
       15. A neuro-fuzzy integrated data processing system according to claim 14, wherein some of said plurality of units provided in said consequent membership function realizing part output a logical sum of a plurality of input values applied to said units.   
     
     
       16. A neuro-fuzzy integrated data processing system according to claim 14, wherein some of said plurality of units provided in said consequent membership function realizing part output an algebraic sum of a plurality of input values applied to said units.   
     
     
       17. A neuro-fuzzy integrated data processing system as recited in claim 11, wherein said at least one non-fuzzy-process calculation realizing part comprises: a center-of-gravity determining element output part comprising two units for receiving a plurality of signals as a result of the enlargement/reduction of a consequent membership function provided by said consequent membership function realizing part; and   a center-of-gravity calculating part for outputting a center-of-gravity value as said system output signal, that is, a representative value, using two center-of-gravity determining elements provided by said center-of-gravity determining element output part.     
     
     
       18. A neuro-fuzzy-integrated data processing system according to claim 17, wherein a first unit of said two units in said center-of-gravity determining element output part outputs 4 the sum of products obtained by multiplying the difference, that is, a weight value (a weight value=2-1 between the minimum value 1 in the abscissa values of a consequent membership function curve corresponding to a plurality of output values as a result of the enlargement/reduction of said consequent membership function and values 2 corresponding to each of a plurality of said output values by output values in a plurality of said output values; and   a second unit of said two units outputs a sum of products obtained by multiplying the difference, that is, a weight value (a weight value=2-3) between the maximum value 3 in the abscissa values corresponding to a plurality of output values and values 2 corresponding to each of a plurality of output values by output values in a plurality of said output values, thus obtaining said center-of-gravity values as follows: ##EQU79##   
     
     
       19. A neuro-fuzzy integrated data processing system according to claim 18, wherein said first and second units output a result by multiplying each of said sums by a constant.   
     
     
       20. A neuro-fuzzy integrated data processing system according to claim 17, wherein a center-of-gravity determining element output unit as said center-of-gravity determining element output part comprises: an endpoint coordinate and oblique storing means for storing coordinates of two endpoints, that is, the maximum and minimum values, in a plurality of abscissa values of said consequent membership function curve and for storing the oblique of a line passing through said center of gravity;   a teaching signal calculating means for obtaining and outputting teaching signals for two center-of-gravity determining elements used to obtain center-of-gravity values using a true center-of-gravity value, said endpoint values stored in said endpoint coordinate and oblique storing means, and an oblique of a line passing through the center-of-gravity; and   a teaching signal determining unit for determining said teaching signals according to an expression of a line passing through a center-of-gravity having a predetermined oblique.     
     
     
       21. A neuro-fuzzy integrated data processing system according to claim 17, wherein a center-of-gravity determining element output unit as said center-of-gravity determining element output part comprises: an endpoint coordinate storing means for storing coordinates of two endpoints, that is, the maximum and minimum abscissa values of said consequent membership function curve;   a teaching signal calculating means for obtaining and outputting teaching signals for two center-of-gravity determining elements used to obtain center-of-gravity values using true center-of-gravity values, two center-of-gravity determining element values, and endpoint coordinates stored in said endpoint coordinate storing means; and   a teaching signal determining unit for determining said teaching signals according to an expression of a line passing through true center-of-gravity values having the same oblique as the line determined by output values of said two center-of-gravity determining elements.     
     
     
       22. A neuro-fuzzy integrated data processing system according to claim 1, wherein said consequent membership function realizing/non-fuzzy-processing part comprises a hierarchical neural network having a plurality of layers.   
     
     
       23. A neuro-fuzzy integrated data processing system according to claim 22, wherein said consequent membership function realizing/non-fuzzy-processing part comprises: a consequent membership function realizing part for enlarging/reducing said consequent membership function where the connections of adjacent layers are set corresponding to said consequent membership functions; and   a center-of-gravity calculation realizing part as a hierarchical neural network having at least one intermediate layer for calculating a center-of-gravity value as said output signal according to the output of said consequent membership function realizing part.     
     
     
       24. A neuro-fuzzy integrated data processing system according to claim 23, wherein an input normalizing unit is provided between said consequent membership function realizing part and said center-of-gravity calculation realizing part for mapping the output of said consequent membership function realizing part within the predetermined range of an input unit in said center-of-gravity calculation realizing part using an appropriate function; and   an output restoring unit is provided between the consequent step of said center-of-gravity calculation realizing part for copying a center-of-gravity coordinate as the output of said center-of-gravity calculation realizing part within an appropriate range of coordinates.   
     
     
       25. A neuro-fuzzy integrated data processing system according to claim 24, wherein said input normalizing unit uses the linear function for mapping according to the following expression:   function value=0.6×(output of consequent membership function realizing part)/10+0.2;     and     said output restoring unit uses other linear functions for mapping according to the following expression:   function value=4×(output of center-of-gravity calculation realizing part-0.2)/0.6+1.       
     
     
       26. A neuro-fuzzy integrated data processing system according to claim 23, wherein random numbers are used as values corresponding to the abscissa of said consequent membership function curve;   teaching data are generated by mapping said random numbers by a linear function within the input range of a hierarchical neural network as said center-of-gravity calculation realizing part, and are supplied as learning data; and   a center-of-gravity learning unit is provided for learning said hierarchical neural network.   
     
     
       27. A neuro-fuzzy integrated data processing system according to claim 26, wherein said center-of-gravity learning unit comprises: a random number generating part for generating random numbers as values corresponding to the abscissa of said consequent membership function curve and   a constant storing part for storing constants including a plurality of abscissa values of said consequent membership function curve and teaching data values; and   a teaching data generating part generates, according to constants stored in said constant storing part and random numbers generated by said random number generating part, teaching data by mapping said random numbers by a linear function within the input range of a hierarchical neural network as said center-of-gravity calculation realizing part.     
     
     
       28. A neuro-fuzzy integrated data processing system according to claim 1, wherein said consequent membership function realizing/non-fuzzy-processing part forms a division network comprising one output unit and at least one intermediate layer;   a weight value of the connection between a unit for outputting a grade value of a fuzzy rule as an enlargement/reduction rate of at least one consequent membership function outputted by said rule part and a first unit of said two input units is set as a value of the abscissa of a consequent membership function curve specified in the consequent part of each fuzzy rule;   a weight value of the connection between a unit for outputting a grade value of said fuzzy rule and a second unit of said two input units is set to 1; and   said division network outputs a result of a sum of input to said first unit divided by a sum of input to the second unit.   
     
     
       29. A neuro-fuzzy integrated data processing system according to claim 28, wherein a weight value of the connection between the input of two input units in said division network can be learned in the back propagation method during the learning process of said consequent membership function realizing/non-fuzzy-processing part.   
     
     
       30. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the upper limit of the sensitivity determined and with a weight value W and a threshold θ defined as follows:   W=4/(b-a), θ=2(a+b)/(b-a)     where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:     Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=1/(1+exp (-WX+θ).       
     
     
       31. A neuro-fuzzy integrated data processing system according to claim 1, wherein approximation of a membership function obtained by a neuro-process can be realized with the upper limit of the sensitivity determined and with a weight value W and a threshold θ defined as follows:   W=2/(b-a), θ=(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=0.5+0.5 tanh (WX-θ).       
     
     
       32. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the integration of an absolute value of an error minimized and with a weight value W and a threshold θ defined as follows:   W=5.3605/(b-a), θ=2.6802(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       <X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=1/(1+exp (-WX+θ).       
     
     
       33. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the integration of an absolute value of an error minimized and with a weight value W and a threshold θ defined as follows:   W=2.6802/(b-a), θ=1.3401(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=0.5+0.5 tanh (WX-θ).       
     
     
       34. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the integration of two square of an error minimized and with a weight value W and a threshold θ defined as follows:   W=5.3012/(b-a), θ=2.6506(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b<X and       the characteristic of a neuron is defined as follows:   Y=1/(1+exp (-WX+θ).       
     
     
       35. A neuro-fuzzy integrated data processing system according to claims 1, 2, or 3, wherein the approximation of a membership function obtained by a neuro-process can be realized with the integration of two square of an error minimized and with a weight value W and a threshold θ defined as follows:   W=2.6506/(b-a), θ=1.3253(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦x and       the characteristic of a neuron is defined as follows:   Y=0.5+0.5 tanh (WX-θ).       
     
     
       36. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the maximum error minimized and with a weight value W and a threshold θ defined as follows:   W=5.648/(b-a), θ=2.824(a+b)/(b-a)       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=1/(1+exp (-WX+θ)).       
     
     
       37. A neuro-fuzzy integrated data processing system according to claim 1, wherein the approximation of a membership function obtained by a neuro-process can be realized with the maximum error minimized and with a weight value W and a threshold θ defined as follows:   W=2.824/(b-a), θ=1.412(a+b)/(b-a)     where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:     Y=0 when X≦a, Y=(X-a)/(b-a) when       a<X<b, Y=1 when b≦X and       the characteristic of a neuron is defined as follows:   Y=0.5+0.5 tanh (WX-θ).       
     
     
       38. A neuro-fuzzy integrated data processing system according to claim 1, wherein a weight value W and a threshold θ of one of two units in the second layer is defined as follows:   W=4/C, θ=2(2a-2b-c)/C       and a weight value W and a threshold θ of the other unit are defined as follows:   W=-4/C, θ=-2(2a+2b+c)/C       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a-b-c,       Y=(X-(a-b-c))/C when a-b-c<X≦a-b,       Y=1 when a-b<X≦a+b,       Y=-(X-(a+b+c))/C when a+b<X≦a+b+c,       Y=0 when a+b+c<X, and       a three layer hierarchical neural network for realizing the approximation of said membership function with the upper limit of the sensitivity determined as ±1/C, said network comprising: a first layer of one unit for outputting an input value as is;   a second layer of two units having a non-linear characteristic; and   a third layer of one neuron for outputting a result obtained by subtracting 1 from a sum of the output of two neurons in the second layer, wherein the characteristic of a unit in the second layer is:   Y=1/(1+exp (-WX+θ)).     39.       
     
     
       39. A neuro-fuzzy integrated data processing system according to claim 1, wherein a weight value W and a threshold θ of one of two units in the second layer is defined as follows:   W=2/C, θ=(2a-2b-c)/C       and a weight value W and a threshold θ of the other unit are defined as follows:   W=-2/C, θ=-(2a+2b+c)/C       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a-b-c,       Y=(X-(a-b-c))/C when a-b-c<X≦a-b,       Y=1 when a-b<X≦a+b,       Y=-(X-(a+b+c))/C when a+b<X≦a+b+c,       Y=0 when a+b+c<X, and       a three layer hierarchical neural network for approximating said membership function with the upper limit of the sensitivity determined as ±1/C, said network comprising: a first layer of one unit for outputting an input value as is;   a second layer of two units having a non-linear characteristic; and   a third layer of one neuron for outputting a result obtained by subtracting 1 k from a sum of the output of two neurons in the second layer, wherein     the characteristic of a unit in the second layer is:   Y=0.5+0.5 tanh (WX-θ)).       
     
     
       40. A neuro-fuzzy integrated data processing system according to claim 1, wherein a weight value W and a threshold θ of one of two units in the second layer is defined as follows:   W=5.468/C, θ=2.824(2a-2b-c)/C       and a weight value W and a threshold θ of the other unit are defined as follows:   W=-5.468/C, θ=-2.824(2a+2b+c)/C       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a-b-c,       Y=(X-(a-b-c))/C when a-b-c<X≦a-b,       Y=1 when a-b<X≦a+b,       Y=-(X-(a+b+c))/C when a+b<X≦a+b+c,       Y=0 when a+b+c<X, and       a three layer hierarchical neural network for realizing the approximation of said membership function with the maximum error minimized, said network comprising: a first layer of one unit for outputting an input value as is;   a second layer of two units having a non-linear characteristic; and   a third layer of one neuron for outputting a result obtained by subtracting 1 from a sum of the output of two neurons in the second layer, wherein the characteristic of a unit in the second layer is:   Y=1/(1+exp (-WX+θ)).         
     
     
       41. A neuro-fuzzy integrated data processing system according to claim 1, wherein a weight value W and a threshold θ of one of two units in the second layer is defined as follows:   W=2.824/C, θ=1.412(2a-2b-c)/C       and a weight value W and a threshold θ of the other unit are defined as follows:   W=-2.824/C, θ=1.412 (2a+2b+c)/C       where said antecedent membership function is defined as having the relation between an input X and an output Y as follows:   Y=0 when X≦a-b-c,       Y=(X-(a-b-c))/C when a-b-c<X≦a-b,       Y=1 when a-b<X≦a+b,       Y=-(X-(a+b+c))/C when a+b<X≦a+b+c,       Y=0 when a+b+c<X, and       a three layer hierarchical neural network for realizing the approximation of said membership function with the maximum error minimized, said network comprising: a first layer of one unit for outputting an input value as is;   a second layer of two units having a non-linear characteristic; and   a third layer of one neuron for outputting a result obtained by subtracting 1 from a sum of the output of two neurons in the second layer, wherein     the characteristic of a unit in the second layer is:   Y=0.5+0.5 tanh (WX-θ)).       
     
     
       42. A network structure conversion system in a hierarchical layered network in a neuro-fuzzy integrated data processing system for calculating and outputting according to a fuzzy control rule the amount of control operation corresponding to the inputted control state value, said system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein at least said rule part comprises a hierarchical neural network; and   the network structure can be simplified by detecting and eliminating insignificant connection to the whole data process between the specified adjacent layers in said hierarchical neural network.     
     
     
       43. A network structure conversion system in a hierarchical network according to claim 42, wherein said hierarchical neural network is a pure neuro at first where all adjacent layers are completely connected; and   said pure neuro is converted to a completely-connected-rule-part neuro where complete connection is made only between said antecedent membership function realizing part and said rule part, between adjacent parts in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; while insignificant connection to the whole data process is deleted between all the other parts in said antecedent membership function realizing part and consequent membership function realizing/non-fuzzy-processing part.   
     
     
       44. A hierarchical network structure conversion system according to claim 42, wherein said hierarchical neural network is a pure neuro at first where all adjacent layers are completely connected; and   said pure neuro is converted to a pre-wired-rule part neuro where complete connection is made betweensaid antecedent membership function realizing part and said rule part, between adjacent parts in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; while insignificant connection to the whole data process is deleted between all the other parts in said antecedent membership function realizing part and consequent membership function realizing/non-fuzzy-processing part.   
     
     
       45. A hierarchical network structure conversion system according to claim 42, wherein said hierarchical neural network comprises a neural network at least in said rule part at first, and is a completely-connected-rule-part neuro where complete connection is made only between said antecedent membership function realizing part and said rule part, between adjacent parts in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; and   said completely-connected-rule-part neuro is converted to a pre-wired-rule-part neuro with the insignificant connection to the whole process deleted between said antecedent membership function realizing part and said rule part, between adjacent parts in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part.   
     
     
       46. A fuzzy model extracting system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizer for comprising a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration in which: at least said rule part has a hierarchical neural network;   said hierarchical neural network is a pre-wired-rule-part neuro; and   a fuzzy rule can be extracted from a pre-wired-rule-part by analyzing capabilities of each unit of said rule part forming said pre-wired-rule-part neuro and then associating them with the capabilities of a specific logical element.     
     
     
       47. A fuzzy model extracting system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizer for comprising a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration in which: said input part, antecedent membership function realizing part, rule part, and a part of at least said consequent membership function realizing/non-fuzzy-processing part have a hierarchical neural network;   said hierarchical neural network is a completely-connected-rule-part neuro that is completely connected only between said antecedent membership function realizing part and said rule part, between layers of said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-realizing part, while insignificant connection is deleted between all the other parts in said antecedent membership function realizing part and consequent membership function realizing/non-fuzzy/processing part; and   a membership function is extracted by analyzing said completely-connected-rule-part neuro.     
     
     
       48. A fuzzy model extracting system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration in which: said input part, antecedent membership function realizing part, rule part, and a part of at least said consequent membership-function realizing/non-fuzzy-processing part have a hierarchical neural network;   said hierarchical neural network is a pure neuro at first where all adjacent layers are completely connected; and   said pure neuro is converted to a completely-connected-rule-part neuro where complete connection is made only between said antecedent membership function realizing part and said rule part, between adjacent parts in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; while insignificant connection to the whole data process is deleted between all the other parts in said antecedent membership function realizing part and consequent membership function realizing/non-fuzzy-processing part; and     a membership function is extracted by analyzing said completely-connected-rule-part neuro.   
     
     
       49. A fuzzy model extracting system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizer for comprising a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signal as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration in which: said input part, antecedent membership function realizing part, rule part, and a part of at least said consequent membership function realizing/non-fuzzy-processing part have a hierarchical neural network;   said hierarchical neural network is a pure neuro at first where all adjacent layers are completely connected; and   said pure neuro is converted to a pre-wired-rule-part neuro where insignificant connection to the whole data process is deleted between layers of said antecedent membership function realizing part and said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; and     a membership function is extracted by analyzing said pre-wired-rule-part neuro.   
     
     
       50. A fuzzy model extracting method comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part, preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration-in which: said input part, antecedent membership function realizing part, rule part, and a part of at least said consequent membership function realizing/non-fuzzy-processing part have a hierarchical neural network;   said hierarchical neural network is a pure neuro at first where all adjacent layers are completely connected; and   said pure neuro is converted to a pre-wired-rule-part neuro where insignificant connection to the whole data process is deleted between layers of said antecedent membership function realizing part and said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; and     a fuzzy rule is extracted by analyzing said pre-wired-rule-part neuro.   
     
     
       51. A fuzzy-model extracting system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said-membership function, and for outputting an enlargement/reduction rate of one or a plurality,of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said neuro-fuzzy integrated data processing system for calculating and outputting the control operation value corresponding to the inputted control state value according to a fuzzy control rule has a configuration in which: at least said rule part has a hierarchical neural network;   said hierarchical neural network comprises a completely-connected-rule-part neuro where complete connection is made only between said antecedent membership function realizing part and said rule part, between adjacent layers in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part; and   said completely-connected-rule-part neuro is converted to a pre-wired-rule-part neuro with the insignificant connection to the whole process deleted between said antecedent membership function realizing part and said rule part, between adjacent layers in said rule part, and between said rule part and said consequent membership function realizing/non-fuzzy-processing part, and     a fuzzy rule is extracted by analyzing said pre-wired-rule-part neuro.   
     
     
       52. A neuro-fuzzy integrated data processing system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting one or a plurality of grade values of a antecedent membership function;   a rule part preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting an enlargement/reduction rate of one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said enlargement/reduction rate of a consequent membership function, calculating a non-fuzzy process after enlarging/reducing a consequent membership function according to said enlargement/reduction rate, and outputting a system output signal, wherein   said rule part comprises neurons.   
     
     
       53. A neuro-fuzzy integrated data processing system comprising: an input part for receiving one or a plurality of input signals;   a antecedent membership function realizing part preceded by said input part for outputting, in response to one or a plurality of said input signals, one or a plurality of grade values indicating the applicability of a antecedent membership function;   a rule part preceded by said antecedent membership function realizing part for forming a network having one or a plurality of layers, receiving a grade value of said membership function, and for outputting transformation information for transforming one or a plurality of consequent membership functions corresponding to one or a plurality of system output signals as a grade value of a fuzzy rule; and   a consequent membership function realizing/non-fuzzy-processing part preceded by said rule part for receiving said transformation information of a consequent membership function, calculating a non-fuzzy process after transforming a consequent membership function according to said transformation information, and outputting a system output signal.   
     
     
       54. A neuro-fuzzy integrated data processing system, comprising: an applicable type data processing unit in a hierarchical network configuration having complete connection;   a pre-wired rule part neuro including a rule part, an antecedent membership function part and a consequent membership function part having only necessary connections;   a completely connected rule part neuro including a rule part, an antecedent membership function part and a consequent membership function part having pre-wired connections;   a fuzzy teacher unit having rules and a membership function; and   a conversion unit to convert the fuzzy teacher unit to the pre-wired rule part neuro when the rules of the fuzzy teacher are definite, and to convert the fuzzy teacher unit to the completely connected rule part neuro when the rules of the fuzzy teacher unit are not definite.